Even roles far from the customer, like engineering, make countless micro-decisions. Without an intuitive understanding of customer pull—what they're trying to achieve and why they're blocked—these decisions will likely miss the mark, even when just following a requirements document.

Structured analysis works when you can theorize potential causes and test them. However, for problems where the causes are "unknown unknowns," design thinking is superior. It starts with user empathy and observation to build a theory from the ground up, rather than imposing one prematurely.

Data's role is to reveal reality and identify problems or opportunities (the "what" and "where"). It cannot prescribe the solution. The creative, inventive process of design is still required to determine "how" to solve the problem effectively.

Product managers frequently receive solutions, not problems, from stakeholders. Instead of saying no, the effective approach is to reframe the solution as a set of assumptions and build a discovery backlog to systematically test them. This builds alignment and leads to better outcomes.

Contrary to typical agile discovery, projects in high-stakes environments benefit from starting with extremely strict processes and documentation. This establishes a compliant foundation. Flexibility can be introduced later, once core requirements and constraints are fully mastered, rather than starting loose and adding rigor.

When handed a specific solution to build, don't just execute. Reverse-engineer the intended customer behavior and outcome. This creates an opportunity to define better success metrics, pressure-test the underlying problem, and potentially propose more effective solutions in the future.

Clients often provide solutions disguised as requirements, like "we need an 8-hour battery." By questioning the context—how, where, and for how long the product is actually used—you can uncover the true need. This can lead to a radically different, simpler, and more elegant solution that better serves the user.

The structured, data-driven engineering design process—from problem identification and data collection to solution design and testing—is directly applicable to defining business strategy, achieving goals, and even managing people effectively.

To build successful products, engineering teams must actively translate market needs and user insights into concrete engineering constraints and design tradeoffs. This reframes product-market fit from a vague business concept into a measurable part of the development process, moving beyond pure technical optimization.